When Congress passed the USA Patriot Act in October 2001, it came with a host of new anti-laundering provisions with which financial institutions (FIs) must now comply. But that's not all it included. Within the fine print are new definitions of what types of businesses are considered FIs, and increases in the responsibility for transactions with correspondent banks.

Violations and Penalties

Penalties have been established for violations of geographic targeting orders (GTOs), which is the targeting of FIs in specific geographic regions for stricter record keeping requirements. The use of GTOs allows the government to target areas within the U.S. through which a disproportionate percentage of funds flow to or from illicit destinations.

Money laundering detection software should be capable of meeting compliance regulations of a variety of domestic and international locales in which the financial institution conducts business. The software must be able to accommodate the large variety of regulations within each country in which an institution operates.

It is becoming increasingly difficult to maintain compliance with the myriad of anti-money laundering regulations in the absence of a sophisticated, automated solution. The complexity of the increased regulations coupled with the sheer volume of transactions being evaluated can overwhelm simplistic, rules-based money laundering detection applications.

Rules-based systems use sets of rules to identify suspect activity. They commonly employ filtering technology that allows the matching of customer data to identify transactions benefiting individuals, businesses or countries that are banned or restricted. This filtering allows prohibited assets to be identified at the point of transaction and frozen.

Artificial Intelligence systems use statistical modeling techniques to evaluate the likelihood that a transaction is representative of money laundering. These systems typically use profiles containing the history of the account, client or both. A current transaction is compared against the profile and the degree of difference determines the likelihood of money laundering or other suspicious activity.

Artificial intelligence, adaptive learning systems provide the best, most cost effective method for evaluating potential money laundering in the current environment. And with the cost of non-compliance escalating up to US$1 million, the time to consider a sophisticated solution to money laundering detection has never been more pressing.

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